基于新权重函数的MIX-GARCH-L模型及其应用OA北大核心CHSSCDCSSCICSTPCD

This paper introduces a new weight function and constructs a new volatility model—MIX-GARCH-L model.The new model can fully utilize high and low frequency data to extract more valuable information.In response to the problem of param-eter estimation in the new model,this paper proposes a parameter estimation method for the MIX-GARCH-L model to analyze the theoretical properties of the estimator,proves the corresponding central limit theorem,and uses the Service-Bootstrap method to simulate and test the data performance of the estimator.The proposed model has the following advantages:The new weight func-tion can better automatically adjust the weight allocation for different trading days based on changes in trading characteristics,so that the weight assigned to each high-frequency trading day is consistent with the impact effect of future volatility.The new volatil-ity model can utilize a variety of high-frequency trading data within the same trading process,and make better use of information,so that MIX-GARCH-L model has better prediction accuracy and prediction advantages.The empirical results show that the MSPE value of the MIX-GARCH-L model is significantly smaller than that of the GARCH-RV model and GARCH-M model,in-dicating that the MIX-GARCH-L model not only has better prediction accuracy in model prediction,but also has better perfor-mance in robustness.